Our neural NER model is similar to the model proposed by [1]} and we use the `BIESO' sequence tagging approach. We are not interested in the type of entities, so we use a total of 5 NER tags. We use features based on word embeddings and character embeddings to represent the sentence tokens. These token vectors are passed to a Bi-LSTM layer for encoding. We use separate tag embeddings to represent the NER tags. We concatenate the tag embedding of the previous token with the Bi-LSTM hidden vectors and pass the concatenated vector to a feed-forward layer with softmax to classify the tags. We train this NER model on the corresponding training data with the sentences and entity names present in them.
